CAPTAIN workflow continental vs highseas

Visualise CAPTAIN results

R libraries

library(readr)
library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(dplyr)
library(gridExtra)
library(biscale)
library(colorspace)
library(grid)
library(jsonlite)
library(here)

FUSE : conservation prioritiy maps

Budget: 0.3

# Read both RDS files from the Data folder
continental_data <- readRDS(here::here("Data/FUSE_continental_full_results_averaged_budget0.3_replicates10.rds"))
high_seas_data <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.3_replicates10.rds"))

# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"

# Transform both datasets to sf objects and project
continental_sf <- st_as_sf(continental_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

high_seas_sf <- st_as_sf(high_seas_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

# Combine the datasets
combined_sf <- rbind(
  mutate(continental_sf, Region = "Continental Waters"),
  mutate(high_seas_sf, Region = "High Seas")
)

# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)

# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90), 
                    c(180, 90), c(180, -90), c(-180, -90))

# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
  st_sfc(crs = 4326) %>%
  st_sf(data.frame(rgn = 'globe', geom = .)) %>%
  smoothr::densify(max_distance = 0.5) %>%
  st_transform(crs = mcbryde_thomas_2)

# Create base theme
my_theme <- theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.direction = "horizontal",
    legend.box = "vertical",
    legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
    legend.title = element_text(margin = margin(b = 10)),
    panel.background = element_rect(fill = "white", color = NA),
    plot.background = element_rect(fill = "white", color = NA),
    panel.grid = element_blank()
  )

# 1. Continental Waters Plot
continental_plot <- ggplot() +
  geom_sf(data = continental_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in Continental Waters",
       subtitle = "Index: FUSE, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# 2. High Seas Plot
high_seas_plot <- ggplot() +
  geom_sf(data = high_seas_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in High Seas",
       subtitle = "Index: FUSE, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Combined Plot (modified)
combined_plot <- ggplot() +
  geom_sf(data = combined_sf, 
          aes(color = Priority), 
          size = 0.5, 
          alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Combined Conservation Priorities",
       subtitle = "Continental Waters and High Seas\nIndex: FUSE, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Display all three plots
#library(patchwork)
continental_plot 

high_seas_plot 

combined_plot

Budget: 0.1

# Read both RDS files from the Data folder
continental_data <- readRDS(here::here("Data/FUSE_full_results_continental_averaged_budget0.1_replicates10.rds"))
high_seas_data <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.1_replicates10.rds"))

# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"

# Transform both datasets to sf objects and project
continental_sf <- st_as_sf(continental_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

high_seas_sf <- st_as_sf(high_seas_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

# Combine the datasets
combined_sf <- rbind(
  mutate(continental_sf, Region = "Continental Waters"),
  mutate(high_seas_sf, Region = "High Seas")
)

# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)

# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90), 
                    c(180, 90), c(180, -90), c(-180, -90))

# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
  st_sfc(crs = 4326) %>%
  st_sf(data.frame(rgn = 'globe', geom = .)) %>%
  smoothr::densify(max_distance = 0.5) %>%
  st_transform(crs = mcbryde_thomas_2)

# Create base theme
my_theme <- theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.direction = "horizontal",
    legend.box = "vertical",
    legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
    legend.title = element_text(margin = margin(b = 10)),
    panel.background = element_rect(fill = "white", color = NA),
    plot.background = element_rect(fill = "white", color = NA),
    panel.grid = element_blank()
  )

# 1. Continental Waters Plot
continental_plot <- ggplot() +
  geom_sf(data = continental_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in Continental Waters",
       subtitle = "Index: FUSE, Budget: 0.1, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# 2. High Seas Plot
high_seas_plot <- ggplot() +
  geom_sf(data = high_seas_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in High Seas",
       subtitle = "Index: FUSE, Budget: 0.1, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Combined Plot (modified)
combined_plot <- ggplot() +
  geom_sf(data = combined_sf, 
          aes(color = Priority), 
          size = 0.5, 
          alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Combined Conservation Priorities",
       subtitle = "Continental Waters and High Seas\nIndex: FUSE, Budget: 0.1, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Display all three plots
#library(patchwork)
continental_plot 

high_seas_plot 

combined_plot

EDGE2 : conservation prioritiy maps

Budget: 0.3

# Read both RDS files from the Data folder
continental_data <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.3_replicates10.rds"))
high_seas_data <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.3_replicates10.rds"))

# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"

# Transform both datasets to sf objects and project
continental_sf <- st_as_sf(continental_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

high_seas_sf <- st_as_sf(high_seas_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

# Combine the datasets
combined_sf <- rbind(
  mutate(continental_sf, Region = "Continental Waters"),
  mutate(high_seas_sf, Region = "High Seas")
)

# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)

# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90), 
                    c(180, 90), c(180, -90), c(-180, -90))

# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
  st_sfc(crs = 4326) %>%
  st_sf(data.frame(rgn = 'globe', geom = .)) %>%
  smoothr::densify(max_distance = 0.5) %>%
  st_transform(crs = mcbryde_thomas_2)

# Create base theme
my_theme <- theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.direction = "horizontal",
    legend.box = "vertical",
    legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
    legend.title = element_text(margin = margin(b = 10)),
    panel.background = element_rect(fill = "white", color = NA),
    plot.background = element_rect(fill = "white", color = NA),
    panel.grid = element_blank()
  )

# 1. Continental Waters Plot
continental_plot <- ggplot() +
  geom_sf(data = continental_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in Continental Waters",
       subtitle = "Index: EDGE2, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# 2. High Seas Plot
high_seas_plot <- ggplot() +
  geom_sf(data = high_seas_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in High Seas",
       subtitle = "Index: EDGE2, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Combined Plot (modified)
combined_plot <- ggplot() +
  geom_sf(data = combined_sf, 
          aes(color = Priority), 
          size = 0.5, 
          alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Combined Conservation Priorities",
       subtitle = "Continental Waters and High Seas\nIndex: EDGE2, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Display all three plots
#library(patchwork)
continental_plot 

high_seas_plot 

combined_plot

Budget: 0.1

# Read both RDS files from the Data folder
continental_data <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.1_replicates10.rds"))
high_seas_data <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.1_replicates10.rds"))

# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"

# Transform both datasets to sf objects and project
continental_sf <- st_as_sf(continental_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

high_seas_sf <- st_as_sf(high_seas_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

# Combine the datasets
combined_sf <- rbind(
  mutate(continental_sf, Region = "Continental Waters"),
  mutate(high_seas_sf, Region = "High Seas")
)

# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)

# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90), 
                    c(180, 90), c(180, -90), c(-180, -90))

# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
  st_sfc(crs = 4326) %>%
  st_sf(data.frame(rgn = 'globe', geom = .)) %>%
  smoothr::densify(max_distance = 0.5) %>%
  st_transform(crs = mcbryde_thomas_2)

# Create base theme
my_theme <- theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.direction = "horizontal",
    legend.box = "vertical",
    legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
    legend.title = element_text(margin = margin(b = 10)),
    panel.background = element_rect(fill = "white", color = NA),
    plot.background = element_rect(fill = "white", color = NA),
    panel.grid = element_blank()
  )

# 1. Continental Waters Plot
continental_plot <- ggplot() +
  geom_sf(data = continental_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in Continental Waters",
       subtitle = "Index: EDGE2, Budget: 0.1, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# 2. High Seas Plot
high_seas_plot <- ggplot() +
  geom_sf(data = high_seas_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in High Seas",
       subtitle = "Index: EDGE2, Budget: 0.1, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Combined Plot (modified)
combined_plot <- ggplot() +
  geom_sf(data = combined_sf, 
          aes(color = Priority), 
          size = 0.5, 
          alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Combined Conservation Priorities",
       subtitle = "Continental Waters and High Seas\nIndex: EDGE2, Budget: 0.1, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Display all three plots
#library(patchwork)
continental_plot 

high_seas_plot 

combined_plot

FUSE and EDGE2 : conservation prioritiy bivariate maps

Budget: 0.3

# Read all RDS files
edge_continental <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.3_replicates10.rds"))
edge_highseas <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.3_replicates10.rds"))
fuse_continental <- readRDS(here::here("Data/FUSE_continental_full_results_averaged_budget0.3_replicates10.rds"))
fuse_highseas <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.3_replicates10.rds"))

# Get world map data and set projection
world <- ne_countries(scale = "medium", returnclass = "sf")
mcbryde_thomas_2 <- "+proj=mbt_s"

# Function to process and combine data
process_data <- function(edge_data, fuse_data) {
  combined_data <- edge_data %>%
    rename(EDGE_Priority = Priority) %>%
    left_join(fuse_data %>% rename(FUSE_Priority = Priority),
              by = c("Longitude", "Latitude"))
  
  # Normalize priorities to 0-1 range
  combined_data <- combined_data %>%
    mutate(
      EDGE_Priority_Norm = (EDGE_Priority - min(EDGE_Priority)) / (max(EDGE_Priority) - min(EDGE_Priority)),
      FUSE_Priority_Norm = (FUSE_Priority - min(FUSE_Priority)) / (max(FUSE_Priority) - min(FUSE_Priority))
    )
  
  # Transform to sf object
  data_sf <- st_as_sf(combined_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
    st_transform(crs = mcbryde_thomas_2)
  
  return(data_sf)
}

# Process continental and high seas data
continental_sf <- process_data(edge_continental, fuse_continental)
highseas_sf <- process_data(edge_highseas, fuse_highseas)

# Combine continental and high seas data for the combined map
combined_sf <- rbind(continental_sf, highseas_sf)

# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)

# Create color palette
map_pal_raw <- bi_pal(pal = 'PurpleOr', dim = 4, preview = FALSE)
map_pal_mtx <- matrix(map_pal_raw, nrow = 4, ncol = 4)
map_pal_mtx[3, ] <- colorspace::lighten(map_pal_mtx[3, ], .1)
map_pal_mtx[2, ] <- colorspace::lighten(map_pal_mtx[2, ], .2)
map_pal_mtx[1, ] <- colorspace::lighten(map_pal_mtx[1, ], .3)
map_pal_mtx[ , 3] <- colorspace::lighten(map_pal_mtx[ , 3], .1)
map_pal_mtx[ , 2] <- colorspace::lighten(map_pal_mtx[ , 2], .2)
map_pal_mtx[ , 1] <- colorspace::lighten(map_pal_mtx[ , 1], .3)
map_pal_mtx[1, 1] <- '#ffffee'
map_pal <- as.vector(map_pal_mtx) %>% setNames(names(map_pal_raw))

# Color mapping function
get_color <- function(edge, fuse) {
  edge_class <- cut(edge, breaks = c(-Inf, 0.25, 0.5, 0.75, Inf), labels = 1:4)
  fuse_class <- cut(fuse, breaks = c(-Inf, 0.25, 0.5, 0.75, Inf), labels = 1:4)
  return(map_pal[(as.numeric(fuse_class)-1)*4 + as.numeric(edge_class)])
}

# Apply colors to all datasets
continental_sf$new_color <- mapply(get_color, continental_sf$EDGE_Priority_Norm, continental_sf$FUSE_Priority_Norm)
highseas_sf$new_color <- mapply(get_color, highseas_sf$EDGE_Priority_Norm, highseas_sf$FUSE_Priority_Norm)
combined_sf$new_color <- mapply(get_color, combined_sf$EDGE_Priority_Norm, combined_sf$FUSE_Priority_Norm)

# Create plot function
create_bivariate_plot <- function(data_sf, title) {
  ggplot() +
    geom_sf(data = data_sf, aes(color = new_color), size = 0.1, alpha = 1) +
    geom_sf(data = world_projected, fill = "lightgray", color = "lightgray") +
    geom_sf(data = globe_border, fill = NA, color = "grey70", size = 0.5) +
    scale_color_identity() +
    coord_sf() +
    theme_minimal() +
    labs(title = title,
         x = NULL, y = NULL) +
    theme(plot.title = element_text(hjust = 0.5))
}

# Create legend
legend_plot <- bi_legend(pal = map_pal, dim = 4,
                        xlab = 'EDGE2',
                        ylab = 'FUSE')

# Create the individual plots
continental_bivariate <- create_bivariate_plot(continental_sf, "Continental Waters: EDGE2 vs FUSE Priorities")
highseas_bivariate <- create_bivariate_plot(highseas_sf, "High Seas: EDGE2 vs FUSE Priorities")
combined_bivariate_03 <- create_bivariate_plot(combined_sf, "Combined Waters: EDGE2 vs FUSE Priorities; Budget: 0.3")

# Arrange plots with shared legend
layout <- rbind(
  c(1, 1, 1),
  c(2, 2, 2),
  c(3, 3, 3),
  c(4, 4, 4)
)

# Create legend with larger text
legend_plot <- bi_legend(pal = map_pal, dim = 4,
                        xlab = 'EDGE2',
                        ylab = 'FUSE',
                        size = 2) + # Base size for the legend elements
  theme(
    axis.title = element_text(size = 18, face = "bold"), # Larger axis titles
    axis.text = element_blank(),  # Larger axis text
    legend.text = element_text(size = 12), # Larger legend text
    legend.title = element_text(size = 14, face = "bold") # Larger legend title
  )

# Keep the rest of your grid.arrange code the same
combined_plot <- grid.arrange(
  continental_bivariate,
  highseas_bivariate,
  combined_bivariate_03,
  legend_plot,
  layout_matrix = layout,
  heights = c(0.32, 0.32, 0.32, 0.15),
  widths = unit(c(15, 15, 15), "inches"),
  top = textGrob("Bivariate Maps of EDGE2 and FUSE Priorities", 
                 gp = gpar(fontsize = 16, font = 2))
)

# Display the combined plot
#print(combined_plot)

# Save the plot if needed
# ggsave("bivariate_priority_maps_all.png", combined_plot, width = 15, height = 16, dpi = 300)

Budget: 0.1

# Read all RDS files
edge_continental <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.1_replicates10.rds"))
edge_highseas <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.1_replicates10.rds"))
fuse_continental <- readRDS(here::here("Data/FUSE_full_results_continental_averaged_budget0.1_replicates10.rds"))
fuse_highseas <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.1_replicates10.rds"))

# Get world map data and set projection
world <- ne_countries(scale = "medium", returnclass = "sf")
mcbryde_thomas_2 <- "+proj=mbt_s"

# Function to process and combine data
process_data <- function(edge_data, fuse_data) {
  combined_data <- edge_data %>%
    rename(EDGE_Priority = Priority) %>%
    left_join(fuse_data %>% rename(FUSE_Priority = Priority),
              by = c("Longitude", "Latitude"))
  
  # Normalize priorities to 0-1 range
  combined_data <- combined_data %>%
    mutate(
      EDGE_Priority_Norm = (EDGE_Priority - min(EDGE_Priority)) / (max(EDGE_Priority) - min(EDGE_Priority)),
      FUSE_Priority_Norm = (FUSE_Priority - min(FUSE_Priority)) / (max(FUSE_Priority) - min(FUSE_Priority))
    )
  
  # Transform to sf object
  data_sf <- st_as_sf(combined_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
    st_transform(crs = mcbryde_thomas_2)
  
  return(data_sf)
}

# Process continental and high seas data
continental_sf <- process_data(edge_continental, fuse_continental)
highseas_sf <- process_data(edge_highseas, fuse_highseas)

# Combine continental and high seas data for the combined map
combined_sf <- rbind(continental_sf, highseas_sf)

# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)

# Create color palette
map_pal_raw <- bi_pal(pal = 'PurpleOr', dim = 4, preview = FALSE)
map_pal_mtx <- matrix(map_pal_raw, nrow = 4, ncol = 4)
map_pal_mtx[3, ] <- colorspace::lighten(map_pal_mtx[3, ], .1)
map_pal_mtx[2, ] <- colorspace::lighten(map_pal_mtx[2, ], .2)
map_pal_mtx[1, ] <- colorspace::lighten(map_pal_mtx[1, ], .3)
map_pal_mtx[ , 3] <- colorspace::lighten(map_pal_mtx[ , 3], .1)
map_pal_mtx[ , 2] <- colorspace::lighten(map_pal_mtx[ , 2], .2)
map_pal_mtx[ , 1] <- colorspace::lighten(map_pal_mtx[ , 1], .3)
map_pal_mtx[1, 1] <- '#ffffee'
map_pal <- as.vector(map_pal_mtx) %>% setNames(names(map_pal_raw))

# Color mapping function
get_color <- function(edge, fuse) {
  edge_class <- cut(edge, breaks = c(-Inf, 0.25, 0.5, 0.75, Inf), labels = 1:4)
  fuse_class <- cut(fuse, breaks = c(-Inf, 0.25, 0.5, 0.75, Inf), labels = 1:4)
  return(map_pal[(as.numeric(fuse_class)-1)*4 + as.numeric(edge_class)])
}

# Apply colors to all datasets
continental_sf$new_color <- mapply(get_color, continental_sf$EDGE_Priority_Norm, continental_sf$FUSE_Priority_Norm)
highseas_sf$new_color <- mapply(get_color, highseas_sf$EDGE_Priority_Norm, highseas_sf$FUSE_Priority_Norm)
combined_sf$new_color <- mapply(get_color, combined_sf$EDGE_Priority_Norm, combined_sf$FUSE_Priority_Norm)

# Create plot function
create_bivariate_plot <- function(data_sf, title) {
  ggplot() +
    geom_sf(data = data_sf, aes(color = new_color), size = 0.1, alpha = 1) +
    geom_sf(data = world_projected, fill = "lightgray", color = "lightgray") +
    geom_sf(data = globe_border, fill = NA, color = "grey70", size = 0.5) +
    scale_color_identity() +
    coord_sf() +
    theme_minimal() +
    labs(title = title,
         x = NULL, y = NULL) +
    theme(plot.title = element_text(hjust = 0.5))
}

# Create legend
legend_plot <- bi_legend(pal = map_pal, dim = 4,
                        xlab = 'EDGE2',
                        ylab = 'FUSE')

# Create the individual plots
continental_bivariate <- create_bivariate_plot(continental_sf, "Continental Waters: EDGE2 vs FUSE Priorities")
highseas_bivariate <- create_bivariate_plot(highseas_sf, "High Seas: EDGE2 vs FUSE Priorities")
combined_bivariate_01 <- create_bivariate_plot(combined_sf, "Combined Waters: EDGE2 vs FUSE Priorities; Budget: 0.1")

# Arrange plots with shared legend
layout <- rbind(
  c(1, 1, 1),
  c(2, 2, 2),
  c(3, 3, 3),
  c(4, 4, 4)
)

# Create legend with larger text
legend_plot <- bi_legend(pal = map_pal, dim = 4,
                        xlab = 'EDGE2',
                        ylab = 'FUSE',
                        size = 2) + # Base size for the legend elements
  theme(
    axis.title = element_text(size = 18, face = "bold"), # Larger axis titles
    axis.text = element_blank(),  # Larger axis text
    legend.text = element_text(size = 12), # Larger legend text
    legend.title = element_text(size = 14, face = "bold") # Larger legend title
  )

# Keep the rest of your grid.arrange code the same
combined_plot <- grid.arrange(
  continental_bivariate,
  highseas_bivariate,
  combined_bivariate_01,
  legend_plot,
  layout_matrix = layout,
  heights = c(0.32, 0.32, 0.32, 0.15),
  widths = unit(c(15, 15, 15), "inches"),
  top = textGrob("Bivariate Maps of EDGE2 and FUSE Priorities", 
                 gp = gpar(fontsize = 16, font = 2))
)

# Display the combined plot
#print(combined_plot)

# Save the plot if needed
# ggsave("bivariate_priority_maps_all.png", combined_plot, width = 15, height = 16, dpi = 300)

Manuscript maps

layout <- rbind(
  c(1, 1, 1),
  c(2, 2, 2),
  c(3, 3, 3)
)

combined_plot <- grid.arrange(
  combined_bivariate_01,
  combined_bivariate_03,
  legend_plot,
  layout_matrix = layout,
  heights = c(0.32, 0.32, 0.15),
  widths = unit(c(15, 15, 15), "inches"),
  top = textGrob("Bivariate Maps of EDGE2 and FUSE Priorities", 
                 gp = gpar(fontsize = 16, font = 2))
)

# TIFF version
ggsave(here::here("bivariate_maps_comparison.png"), 
       combined_plot,
       width = 10, 
       height = 12, 
       dpi = 300,
       bg = "white")

FUSE : species level priorities

Budget: 0.3

# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-FUSE mapping using the JSON data
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Add FUSE scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac_complete, aes(x = FUSE)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores\n(Continental)",
       x = "FUSE Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = FUSE, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (Continental)",
       x = "FUSE Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-FUSE mapping using the JSON data
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Add FUSE scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac_complete, aes(x = FUSE)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores\n(High Seas)",
       x = "FUSE Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = FUSE, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (High Seas)",
       x = "FUSE Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)

# For the combined analysis part, modify similarly:
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# Get species mappings for both datasets
continental_species_mapping <- continental_sp_data %>%
  distinct(sp, species_name)
highseas_species_mapping <- highseas_sp_data %>%
  distinct(sp, species_name)

# Add species names to both datasets
continental_prot_frac <- continental_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(continental_species_mapping, by = "sp")

highseas_prot_frac <- highseas_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(highseas_species_mapping, by = "sp")

# Calculate range sizes
continental_ranges <- continental_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(continental_range = n(), .groups = "drop")

highseas_ranges <- highseas_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(highseas_range = n(), .groups = "drop")

# Create species-FUSE mapping
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Combine the protection fractions with range sizes
combined_protection <- full_join(
  continental_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(continental_protection = Mean_Protect_Fraction),
  highseas_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(highseas_protection = Mean_Protect_Fraction),
  by = c("sp", "species_name")
) %>%
  # Join with the range sizes
  left_join(continental_ranges, by = c("sp", "species_name")) %>%
  left_join(highseas_ranges, by = c("sp", "species_name")) %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Calculate weighted protection
combined_protection <- combined_protection %>%
  mutate(
    # Replace NA with 0 for protection values and ranges
    continental_protection = replace_na(continental_protection, 0),
    highseas_protection = replace_na(highseas_protection, 0),
    continental_range = replace_na(continental_range, 0),
    highseas_range = replace_na(highseas_range, 0),
    # Calculate total range
    total_range = continental_range + highseas_range,
    # Calculate weighted protection
    weighted_protection = (continental_protection * continental_range + 
                         highseas_protection * highseas_range) / 
                         total_range
  )

# Add FUSE scores
combined_protection <- left_join(combined_protection, species_FUSE_map, by = c("species_name" = "Species"))

# Create summary statistics
summary_stats <- combined_protection %>%
  select(-species_name) %>%  # Remove the species name column as it's not numerical
  summarise(across(everything(), list(
    min = ~min(., na.rm = TRUE),
    q1 = ~quantile(., 0.25, na.rm = TRUE),
    median = ~median(., na.rm = TRUE),
    mean = ~mean(., na.rm = TRUE),
    q3 = ~quantile(., 0.75, na.rm = TRUE),
    max = ~max(., na.rm = TRUE)
  ))) %>%
  pivot_longer(everything(), 
               names_to = c("variable", "stat"), 
               names_pattern = "(.*)_(.*)") %>%
  pivot_wider(names_from = stat, values_from = value)

# Create and format the flextable
library(flextable)

summary_table <- flextable(summary_stats) %>%
  set_header_labels(
    variable = "Variable",
    min = "Minimum",
    q1 = "1st Quartile",
    median = "Median",
    mean = "Mean",
    q3 = "3rd Quartile",
    max = "Maximum"
  ) %>%
  colformat_double(digits = 3) %>%  # Format numbers to 3 decimal places
  theme_vanilla() %>%
  autofit()

# Display the table
summary_table

Variable	Minimum	1st Quartile	Median	Mean	3rd Quartile	Maximum
sp	1.000	269.000	530.000	534.971	798.000	1,075.000
continental_protection	0.000	0.302	0.308	0.352	0.336	1.000
highseas_protection	0.000	0.000	0.000	0.101	0.000	1.000
continental_range	0.000	56.000	193.000	1,248.928	650.000	40,875.000
highseas_range	0.000	0.000	0.000	805.935	0.000	63,442.000
FUSE.x	0.000	0.000	0.001	0.059	0.031	1.000
total_range	1.000	56.000	200.000	2,054.864	655.000	104,317.000
weighted_protection	0.300	0.303	0.309	0.356	0.341	1.000
FUSE.y	0.000	0.000	0.001	0.059	0.031	1.000

# Create visualizations
hist_protect <- ggplot(combined_protection, aes(x = weighted_protection)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Range-Weighted Protection \nFraction (Combined)",
       x = "Weighted Protection Fraction",
       y = "Count")

hist_fuse <- ggplot(combined_protection, aes(x = FUSE.x)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores",
       x = "FUSE Score",
       y = "Count")

scatter_plot <- ggplot(combined_protection, aes(x = FUSE.x, y = weighted_protection)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Weighted Protection Fraction (Combined)",
       x = "FUSE Score",
       y = "Weighted Protection Fraction")

# Create species range type summary
range_type_summary <- combined_protection %>%
  summarise(
    total_species = n(),
    continental_only = sum(highseas_range == 0 & continental_range > 0),
    highseas_only = sum(continental_range == 0 & highseas_range > 0),
    both_ranges = sum(continental_range > 0 & highseas_range > 0)
  ) %>%
  pivot_longer(everything(), 
               names_to = "Distribution Type",
               values_to = "Number of Species") 

# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
  set_header_labels(
    `Distribution Type` = "Distribution Type",
    `Number of Species` = "Number of Species"
  ) %>%
  theme_vanilla() %>%
  autofit()

# Display the table
range_type_table

Distribution Type	Number of Species
total_species	1,005
continental_only	802
highseas_only	5
both_ranges	198

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

# Save the combined protection data
#saveRDS(combined_protection, file = here::here("Data", "combined_protection_analysis.rds"))

Budget: 0.1

# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_01_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-FUSE mapping using the JSON data
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Add FUSE scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac_complete, aes(x = FUSE)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores\n(Continental)",
       x = "FUSE Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = FUSE, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (Continental)",
       x = "FUSE Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_01_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-FUSE mapping using the JSON data
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Add FUSE scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac_complete, aes(x = FUSE)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores\n(High Seas)",
       x = "FUSE Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = FUSE, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (High Seas)",
       x = "FUSE Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)

# For the combined analysis part, modify similarly:
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_01_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_01_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# Get species mappings for both datasets
continental_species_mapping <- continental_sp_data %>%
  distinct(sp, species_name)
highseas_species_mapping <- highseas_sp_data %>%
  distinct(sp, species_name)

# Add species names to both datasets
continental_prot_frac <- continental_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(continental_species_mapping, by = "sp")

highseas_prot_frac <- highseas_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(highseas_species_mapping, by = "sp")

# Calculate range sizes
continental_ranges <- continental_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(continental_range = n(), .groups = "drop")

highseas_ranges <- highseas_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(highseas_range = n(), .groups = "drop")

# Create species-FUSE mapping
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Combine the protection fractions with range sizes
combined_protection <- full_join(
  continental_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(continental_protection = Mean_Protect_Fraction),
  highseas_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(highseas_protection = Mean_Protect_Fraction),
  by = c("sp", "species_name")
) %>%
  # Join with the range sizes
  left_join(continental_ranges, by = c("sp", "species_name")) %>%
  left_join(highseas_ranges, by = c("sp", "species_name")) %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Calculate weighted protection
combined_protection <- combined_protection %>%
  mutate(
    # Replace NA with 0 for protection values and ranges
    continental_protection = replace_na(continental_protection, 0),
    highseas_protection = replace_na(highseas_protection, 0),
    continental_range = replace_na(continental_range, 0),
    highseas_range = replace_na(highseas_range, 0),
    # Calculate total range
    total_range = continental_range + highseas_range,
    # Calculate weighted protection
    weighted_protection = (continental_protection * continental_range + 
                         highseas_protection * highseas_range) / 
                         total_range
  )

# Add FUSE scores
combined_protection <- left_join(combined_protection, species_FUSE_map, by = c("species_name" = "Species"))

# Create summary statistics
summary_stats <- combined_protection %>%
  select(-species_name) %>%  # Remove the species name column as it's not numerical
  summarise(across(everything(), list(
    min = ~min(., na.rm = TRUE),
    q1 = ~quantile(., 0.25, na.rm = TRUE),
    median = ~median(., na.rm = TRUE),
    mean = ~mean(., na.rm = TRUE),
    q3 = ~quantile(., 0.75, na.rm = TRUE),
    max = ~max(., na.rm = TRUE)
  ))) %>%
  pivot_longer(everything(), 
               names_to = c("variable", "stat"), 
               names_pattern = "(.*)_(.*)") %>%
  pivot_wider(names_from = stat, values_from = value)

# Create and format the flextable
library(flextable)

summary_table <- flextable(summary_stats) %>%
  set_header_labels(
    variable = "Variable",
    min = "Minimum",
    q1 = "1st Quartile",
    median = "Median",
    mean = "Mean",
    q3 = "3rd Quartile",
    max = "Maximum"
  ) %>%
  colformat_double(digits = 3) %>%  # Format numbers to 3 decimal places
  theme_vanilla() %>%
  autofit()

# Display the table
summary_table

Variable	Minimum	1st Quartile	Median	Mean	3rd Quartile	Maximum
sp	1.000	269.000	530.000	534.971	798.000	1,075.000
continental_protection	0.000	0.103	0.110	0.187	0.194	1.000
highseas_protection	0.000	0.000	0.000	0.075	0.000	1.000
continental_range	0.000	56.000	193.000	1,248.928	650.000	40,875.000
highseas_range	0.000	0.000	0.000	805.935	0.000	63,442.000
FUSE.x	0.000	0.000	0.001	0.059	0.031	1.000
total_range	1.000	56.000	200.000	2,054.864	655.000	104,317.000
weighted_protection	0.100	0.104	0.112	0.191	0.200	1.000
FUSE.y	0.000	0.000	0.001	0.059	0.031	1.000

# Create visualizations
hist_protect <- ggplot(combined_protection, aes(x = weighted_protection)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Range-Weighted Protection \nFraction (Combined)",
       x = "Weighted Protection Fraction",
       y = "Count")

hist_fuse <- ggplot(combined_protection, aes(x = FUSE.x)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores",
       x = "FUSE Score",
       y = "Count")

scatter_plot <- ggplot(combined_protection, aes(x = FUSE.x, y = weighted_protection)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Weighted Protection Fraction (Combined)",
       x = "FUSE Score",
       y = "Weighted Protection Fraction")

# Create species range type summary
range_type_summary <- combined_protection %>%
  summarise(
    total_species = n(),
    continental_only = sum(highseas_range == 0 & continental_range > 0),
    highseas_only = sum(continental_range == 0 & highseas_range > 0),
    both_ranges = sum(continental_range > 0 & highseas_range > 0)
  ) %>%
  pivot_longer(everything(), 
               names_to = "Distribution Type",
               values_to = "Number of Species") 

# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
  set_header_labels(
    `Distribution Type` = "Distribution Type",
    `Number of Species` = "Number of Species"
  ) %>%
  theme_vanilla() %>%
  autofit()

# Display the table
range_type_table

Distribution Type	Number of Species
total_species	1,005
continental_only	802
highseas_only	5
both_ranges	198

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

# Save the combined protection data
#saveRDS(combined_protection, file = here::here("Data", "combined_protection_analysis.rds"))

EDGE2 : species level priorities

Budget: 0.3

# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_03_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-EDGE2 mapping using the JSON data
species_EDGE2_map <- data.frame(
  Species = sp$EDGE2$info$Species,
  EDGE2 = as.numeric(sp$EDGE2$info$EDGE2)
)

# Add EDGE2 scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_EDGE2_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for EDGE2
hist_EDGE2 <- ggplot(prot_frac_complete, aes(x = EDGE2)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of EDGE2 Scores\n(Continental)",
       x = "EDGE2 Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = EDGE2, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: EDGE2 vs Mean Protect Fraction (Continental)",
       x = "EDGE2 Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_EDGE2, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-EDGE2 mapping using the JSON data
species_EDGE2_map <- data.frame(
  Species = sp$EDGE2$info$Species,
  EDGE2 = as.numeric(sp$EDGE2$info$EDGE2)
)

# Add EDGE2 scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_EDGE2_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for EDGE2
hist_EDGE2 <- ggplot(prot_frac_complete, aes(x = EDGE2)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of EDGE2 Scores\n(High Seas)",
       x = "EDGE2 Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = EDGE2, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: EDGE2 vs Mean Protect Fraction (High Seas)",
       x = "EDGE2 Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_EDGE2, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)

# For the combined analysis part, modify similarly:
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_03_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# Get species mappings for both datasets
continental_species_mapping <- continental_sp_data %>%
  distinct(sp, species_name)
highseas_species_mapping <- highseas_sp_data %>%
  distinct(sp, species_name)

# Add species names to both datasets
continental_prot_frac <- continental_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(continental_species_mapping, by = "sp")

highseas_prot_frac <- highseas_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(highseas_species_mapping, by = "sp")

# Calculate range sizes
continental_ranges <- continental_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(continental_range = n(), .groups = "drop")

highseas_ranges <- highseas_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(highseas_range = n(), .groups = "drop")

# Create species-EDGE2 mapping
species_EDGE2_map <- data.frame(
  Species = sp$EDGE2$info$Species,
  EDGE2 = as.numeric(sp$EDGE2$info$EDGE2)
)

# Combine the protection fractions with range sizes
combined_protection <- full_join(
  continental_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(continental_protection = Mean_Protect_Fraction),
  highseas_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(highseas_protection = Mean_Protect_Fraction),
  by = c("sp", "species_name")
) %>%
  # Join with the range sizes
  left_join(continental_ranges, by = c("sp", "species_name")) %>%
  left_join(highseas_ranges, by = c("sp", "species_name")) %>%
  left_join(species_EDGE2_map, by = c("species_name" = "Species"))

# Calculate weighted protection
combined_protection <- combined_protection %>%
  mutate(
    # Replace NA with 0 for protection values and ranges
    continental_protection = replace_na(continental_protection, 0),
    highseas_protection = replace_na(highseas_protection, 0),
    continental_range = replace_na(continental_range, 0),
    highseas_range = replace_na(highseas_range, 0),
    # Calculate total range
    total_range = continental_range + highseas_range,
    # Calculate weighted protection
    weighted_protection = (continental_protection * continental_range + 
                         highseas_protection * highseas_range) / 
                         total_range
  )

# Add EDGE2 scores
combined_protection <- left_join(combined_protection, species_EDGE2_map, by = c("species_name" = "Species"))

# Create summary statistics
summary_stats <- combined_protection %>%
  select(-species_name) %>%  # Remove the species name column as it's not numerical
  summarise(across(everything(), list(
    min = ~min(., na.rm = TRUE),
    q1 = ~quantile(., 0.25, na.rm = TRUE),
    median = ~median(., na.rm = TRUE),
    mean = ~mean(., na.rm = TRUE),
    q3 = ~quantile(., 0.75, na.rm = TRUE),
    max = ~max(., na.rm = TRUE)
  ))) %>%
  pivot_longer(everything(), 
               names_to = c("variable", "stat"), 
               names_pattern = "(.*)_(.*)") %>%
  pivot_wider(names_from = stat, values_from = value)

# Create and format the flextable
library(flextable)

summary_table <- flextable(summary_stats) %>%
  set_header_labels(
    variable = "Variable",
    min = "Minimum",
    q1 = "1st Quartile",
    median = "Median",
    mean = "Mean",
    q3 = "3rd Quartile",
    max = "Maximum"
  ) %>%
  colformat_double(digits = 3) %>%  # Format numbers to 3 decimal places
  theme_vanilla() %>%
  autofit()

# Display the table
summary_table

Variable	Minimum	1st Quartile	Median	Mean	3rd Quartile	Maximum
sp	1.000	269.000	530.000	534.971	798.000	1,075.000
continental_protection	0.000	0.302	0.307	0.348	0.330	1.000
highseas_protection	0.000	0.000	0.000	0.096	0.000	1.000
continental_range	0.000	56.000	193.000	1,248.928	650.000	40,875.000
highseas_range	0.000	0.000	0.000	805.935	0.000	63,442.000
EDGE2.x	0.000	0.000	0.001	0.045	0.018	1.000
total_range	1.000	56.000	200.000	2,054.864	655.000	104,317.000
weighted_protection	0.300	0.303	0.308	0.350	0.331	1.000
EDGE2.y	0.000	0.000	0.001	0.045	0.018	1.000

# Create visualizations
hist_protect <- ggplot(combined_protection, aes(x = weighted_protection)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Range-Weighted Protection \nFraction (Combined)",
       x = "Weighted Protection Fraction",
       y = "Count")

hist_EDGE2 <- ggplot(combined_protection, aes(x = EDGE2.x)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of EDGE2 Scores",
       x = "EDGE2 Score",
       y = "Count")

scatter_plot <- ggplot(combined_protection, aes(x = EDGE2.x, y = weighted_protection)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: EDGE2 vs Weighted Protection Fraction (Combined)",
       x = "EDGE2 Score",
       y = "Weighted Protection Fraction")

# Create species range type summary
range_type_summary <- combined_protection %>%
  summarise(
    total_species = n(),
    continental_only = sum(highseas_range == 0 & continental_range > 0),
    highseas_only = sum(continental_range == 0 & highseas_range > 0),
    both_ranges = sum(continental_range > 0 & highseas_range > 0)
  ) %>%
  pivot_longer(everything(), 
               names_to = "Distribution Type",
               values_to = "Number of Species") 

# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
  set_header_labels(
    `Distribution Type` = "Distribution Type",
    `Number of Species` = "Number of Species"
  ) %>%
  theme_vanilla() %>%
  autofit()

# Display the table
range_type_table

Distribution Type	Number of Species
total_species	1,005
continental_only	802
highseas_only	5
both_ranges	198

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_EDGE2, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

# Save the combined protection data
#saveRDS(combined_protection, file = here::here("Data", "combined_protection_analysis.rds"))

Budget: 0.1

# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_01_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-EDGE2 mapping using the JSON data
species_EDGE2_map <- data.frame(
  Species = sp$EDGE2$info$Species,
  EDGE2 = as.numeric(sp$EDGE2$info$EDGE2)
)

# Add EDGE2 scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_EDGE2_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for EDGE2
hist_EDGE2 <- ggplot(prot_frac_complete, aes(x = EDGE2)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of EDGE2 Scores\n(Continental)",
       x = "EDGE2 Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = EDGE2, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: EDGE2 vs Mean Protect Fraction (Continental)",
       x = "EDGE2 Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_EDGE2, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_01_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-EDGE2 mapping using the JSON data
species_EDGE2_map <- data.frame(
  Species = sp$EDGE2$info$Species,
  EDGE2 = as.numeric(sp$EDGE2$info$EDGE2)
)

# Add EDGE2 scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_EDGE2_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for EDGE2
hist_EDGE2 <- ggplot(prot_frac_complete, aes(x = EDGE2)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of EDGE2 Scores\n(High Seas)",
       x = "EDGE2 Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = EDGE2, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: EDGE2 vs Mean Protect Fraction (High Seas)",
       x = "EDGE2 Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_EDGE2, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)

# For the combined analysis part, modify similarly:
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_01_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_01_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# Get species mappings for both datasets
continental_species_mapping <- continental_sp_data %>%
  distinct(sp, species_name)
highseas_species_mapping <- highseas_sp_data %>%
  distinct(sp, species_name)

# Add species names to both datasets
continental_prot_frac <- continental_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(continental_species_mapping, by = "sp")

highseas_prot_frac <- highseas_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(highseas_species_mapping, by = "sp")

# Calculate range sizes
continental_ranges <- continental_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(continental_range = n(), .groups = "drop")

highseas_ranges <- highseas_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(highseas_range = n(), .groups = "drop")

# Create species-EDGE2 mapping
species_EDGE2_map <- data.frame(
  Species = sp$EDGE2$info$Species,
  EDGE2 = as.numeric(sp$EDGE2$info$EDGE2)
)

# Combine the protection fractions with range sizes
combined_protection <- full_join(
  continental_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(continental_protection = Mean_Protect_Fraction),
  highseas_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(highseas_protection = Mean_Protect_Fraction),
  by = c("sp", "species_name")
) %>%
  # Join with the range sizes
  left_join(continental_ranges, by = c("sp", "species_name")) %>%
  left_join(highseas_ranges, by = c("sp", "species_name")) %>%
  left_join(species_EDGE2_map, by = c("species_name" = "Species"))

# Calculate weighted protection
combined_protection <- combined_protection %>%
  mutate(
    # Replace NA with 0 for protection values and ranges
    continental_protection = replace_na(continental_protection, 0),
    highseas_protection = replace_na(highseas_protection, 0),
    continental_range = replace_na(continental_range, 0),
    highseas_range = replace_na(highseas_range, 0),
    # Calculate total range
    total_range = continental_range + highseas_range,
    # Calculate weighted protection
    weighted_protection = (continental_protection * continental_range + 
                         highseas_protection * highseas_range) / 
                         total_range
  )

# Add EDGE2 scores
combined_protection <- left_join(combined_protection, species_EDGE2_map, by = c("species_name" = "Species"))

# Create summary statistics
summary_stats <- combined_protection %>%
  select(-species_name) %>%  # Remove the species name column as it's not numerical
  summarise(across(everything(), list(
    min = ~min(., na.rm = TRUE),
    q1 = ~quantile(., 0.25, na.rm = TRUE),
    median = ~median(., na.rm = TRUE),
    mean = ~mean(., na.rm = TRUE),
    q3 = ~quantile(., 0.75, na.rm = TRUE),
    max = ~max(., na.rm = TRUE)
  ))) %>%
  pivot_longer(everything(), 
               names_to = c("variable", "stat"), 
               names_pattern = "(.*)_(.*)") %>%
  pivot_wider(names_from = stat, values_from = value)

# Create and format the flextable
library(flextable)

summary_table <- flextable(summary_stats) %>%
  set_header_labels(
    variable = "Variable",
    min = "Minimum",
    q1 = "1st Quartile",
    median = "Median",
    mean = "Mean",
    q3 = "3rd Quartile",
    max = "Maximum"
  ) %>%
  colformat_double(digits = 3) %>%  # Format numbers to 3 decimal places
  theme_vanilla() %>%
  autofit()

# Display the table
summary_table

Variable	Minimum	1st Quartile	Median	Mean	3rd Quartile	Maximum
sp	1.000	269.000	530.000	534.971	798.000	1,075.000
continental_protection	0.000	0.103	0.109	0.174	0.160	1.000
highseas_protection	0.000	0.000	0.000	0.068	0.000	1.000
continental_range	0.000	56.000	193.000	1,248.928	650.000	40,875.000
highseas_range	0.000	0.000	0.000	805.935	0.000	63,442.000
EDGE2.x	0.000	0.000	0.001	0.045	0.018	1.000
total_range	1.000	56.000	200.000	2,054.864	655.000	104,317.000
weighted_protection	0.100	0.104	0.110	0.176	0.165	1.000
EDGE2.y	0.000	0.000	0.001	0.045	0.018	1.000

# Create visualizations
hist_protect <- ggplot(combined_protection, aes(x = weighted_protection)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Range-Weighted Protection \nFraction (Combined)",
       x = "Weighted Protection Fraction",
       y = "Count")

hist_EDGE2 <- ggplot(combined_protection, aes(x = EDGE2.x)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of EDGE2 Scores",
       x = "EDGE2 Score",
       y = "Count")

scatter_plot <- ggplot(combined_protection, aes(x = EDGE2.x, y = weighted_protection)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: EDGE2 vs Weighted Protection Fraction (Combined)",
       x = "EDGE2 Score",
       y = "Weighted Protection Fraction")

# Create species range type summary
range_type_summary <- combined_protection %>%
  summarise(
    total_species = n(),
    continental_only = sum(highseas_range == 0 & continental_range > 0),
    highseas_only = sum(continental_range == 0 & highseas_range > 0),
    both_ranges = sum(continental_range > 0 & highseas_range > 0)
  ) %>%
  pivot_longer(everything(), 
               names_to = "Distribution Type",
               values_to = "Number of Species") 

# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
  set_header_labels(
    `Distribution Type` = "Distribution Type",
    `Number of Species` = "Number of Species"
  ) %>%
  theme_vanilla() %>%
  autofit()

# Display the table
range_type_table

Distribution Type	Number of Species
total_species	1,005
continental_only	802
highseas_only	5
both_ranges	198

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_EDGE2, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

# Save the combined protection data
#saveRDS(combined_protection, file = here::here("Data", "combined_protection_analysis.rds"))